A generative framework for predicting cellular morphological and transcriptomic perturbation responses

Predicting coordinated cellular responses to perturbations is crucial for phenotypic drug discovery and building virtual cell models. Current methods typically predict isolated modalities or require empirical post-perturbation data, precluding true de novo virtual screening. To address this, we introduce MultiVCDiff, a unified multimodal generative diffusion framework that simultaneously predicts high-fidelity cellular morphology images and transcriptomic profiles directly from chemical or genetic perturbations. This framework was trained on a comprehensive trimodal corpus encompassing 1,118 chemical and 130 genetic perturbations across four datasets. Evaluated under strict zero-shot settings on unseen drugs, MultiVCDiff outperforms state-of-the-art single-modality baselines. It successfully overcomes morphological mode collapse, generates highly accurate gene expression signatures, and captures complex biological relationships to reliably retrieve drug mechanisms of action. By eliminating the dependency on prior post-perturbation readouts, MultiVCDiff enables scalable in silico hypothesis generation, significantly accelerating drug discovery and advancing holistic virtual cell modeling.